Rapid epidemic expansion of the SARS-CoV-2 Omicron BA.2 subvariant during China’s largest outbreaks
Bibliographic record
Abstract
Abstract A complete and accurate statistical panorama and analysis of cases contracted with theSARS-CoV-2 Omicron BA.2 subvariant are given under the conditions of strict mandatoryquarantine and isolation and of a high rate of full vaccination. Sars-Cov-2 is still new, and little isknown about either its direction of variation or its propagation laws. No country other than Chinahas been able to disclose every infected case and to have the data of heavily intervening largeoutbreaks. Here, my study reveals that the BA.2 subvariant can still spread very fast and wide inareas with strict “dynamic zero-COVID strategy”i in China, that there exist in different cities asmany as twenty-time large differences in morbidity rate unrelated to any of the influence factorsknown and that the Omicron BA.2 subvariant is unpredictable of its virulence, although its severerate of confirmed cases is low. This analysis provides first-hand solitary and valuable informationfor further research on similar epidemics in the future. It may bring new thoughts for correctionof present epidemiological theory and mathematical models. It may also give other countriestime to be better prepared for the coming 6th wave driven by Omicron BA.2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".